Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.
Estimated Time Needed: 30 min
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import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.
Tesla = yf.Ticker('TSLA')
Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.
tesla_data = Tesla.history(period = 'max')
print(tesla_data)
Open High Low Close Volume \
Date
2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500
2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500
2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000
2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000
2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500
... ... ... ... ... ...
2024-01-22 212.259995 217.800003 206.270004 208.800003 117952500
2024-01-23 211.300003 215.649994 207.750000 209.139999 106605900
2024-01-24 211.880005 212.729996 206.770004 207.830002 123369900
2024-01-25 189.699997 193.000000 180.059998 182.630005 198076800
2024-01-26 185.500000 186.779999 182.100006 182.820099 93262176
Dividends Stock Splits
Date
2010-06-29 0 0.0
2010-06-30 0 0.0
2010-07-01 0 0.0
2010-07-02 0 0.0
2010-07-06 0 0.0
... ... ...
2024-01-22 0 0.0
2024-01-23 0 0.0
2024-01-24 0 0.0
2024-01-25 0 0.0
2024-01-26 0 0.0
[3418 rows x 7 columns]
Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.
tesla_data.reset_index(inplace=True)
tesla_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0 | 0.0 |
| 1 | 2010-06-30 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0 | 0.0 |
| 2 | 2010-07-01 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0 | 0.0 |
| 3 | 2010-07-02 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0 | 0.0 |
| 4 | 2010-07-06 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0 | 0.0 |
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data = requests.get(url).text
Parse the html data using beautiful_soup.
soup = BeautifulSoup(html_data,'html.parser')
Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
tesla_revenue = pd.DataFrame(columns=['Date','Revenue'])
for row in soup.find_all("tbody")[1].find_all('tr'):
col=row.find_all("td")
date=col[0].text
revenue=col[1].text
tesla_revenue=tesla_revenue.append({"Date":date,"Revenue":revenue},ignore_index=True)
tesla_revenue
#
print(tesla_revenue)
Date Revenue 0 2020-04-30 $1,021 1 2020-01-31 $2,194 2 2019-10-31 $1,439 3 2019-07-31 $1,286 4 2019-04-30 $1,548 .. ... ... 57 2006-01-31 $1,667 58 2005-10-31 $534 59 2005-07-31 $416 60 2005-04-30 $475 61 2005-01-31 $709 [62 rows x 2 columns]
Execute the following line to remove the comma and dollar sign from the Revenue column.
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")
print(tesla_revenue)
Date Revenue 0 2022-09-30 21454 1 2022-06-30 16934 2 2022-03-31 18756 3 2021-12-31 17719 4 2021-09-30 13757 5 2021-06-30 11958 6 2021-03-31 10389 7 2020-12-31 10744 8 2020-09-30 8771 9 2020-06-30 6036 10 2020-03-31 5985 11 2019-12-31 7384 12 2019-09-30 6303 13 2019-06-30 6350 14 2019-03-31 4541 15 2018-12-31 7226 16 2018-09-30 6824 17 2018-06-30 4002 18 2018-03-31 3409 19 2017-12-31 3288 20 2017-09-30 2985 21 2017-06-30 2790 22 2017-03-31 2696 23 2016-12-31 2285 24 2016-09-30 2298 25 2016-06-30 1270 26 2016-03-31 1147 27 2015-12-31 1214 28 2015-09-30 937 29 2015-06-30 955 30 2015-03-31 940 31 2014-12-31 957 32 2014-09-30 852 33 2014-06-30 769 34 2014-03-31 621 35 2013-12-31 615 36 2013-09-30 431 37 2013-06-30 405 38 2013-03-31 562 39 2012-12-31 306 40 2012-09-30 50 41 2012-06-30 27 42 2012-03-31 30 43 2011-12-31 39 44 2011-09-30 58 45 2011-06-30 58 46 2011-03-31 49 47 2010-12-31 36 48 2010-09-30 31 49 2010-06-30 28 50 2010-03-31 21 52 2009-09-30 46 53 2009-06-30 27
Execute the following lines to remove an null or empty strings in the Revenue column.
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
print(tesla_revenue)
Date Revenue 0 2022-09-30 21454 1 2022-06-30 16934 2 2022-03-31 18756 3 2021-12-31 17719 4 2021-09-30 13757 5 2021-06-30 11958 6 2021-03-31 10389 7 2020-12-31 10744 8 2020-09-30 8771 9 2020-06-30 6036 10 2020-03-31 5985 11 2019-12-31 7384 12 2019-09-30 6303 13 2019-06-30 6350 14 2019-03-31 4541 15 2018-12-31 7226 16 2018-09-30 6824 17 2018-06-30 4002 18 2018-03-31 3409 19 2017-12-31 3288 20 2017-09-30 2985 21 2017-06-30 2790 22 2017-03-31 2696 23 2016-12-31 2285 24 2016-09-30 2298 25 2016-06-30 1270 26 2016-03-31 1147 27 2015-12-31 1214 28 2015-09-30 937 29 2015-06-30 955 30 2015-03-31 940 31 2014-12-31 957 32 2014-09-30 852 33 2014-06-30 769 34 2014-03-31 621 35 2013-12-31 615 36 2013-09-30 431 37 2013-06-30 405 38 2013-03-31 562 39 2012-12-31 306 40 2012-09-30 50 41 2012-06-30 27 42 2012-03-31 30 43 2011-12-31 39 44 2011-09-30 58 45 2011-06-30 58 46 2011-03-31 49 47 2010-12-31 36 48 2010-09-30 31 49 2010-06-30 28 50 2010-03-31 21 52 2009-09-30 46 53 2009-06-30 27
Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.
tesla_revenue.tail(5)
| Date | Revenue | |
|---|---|---|
| 48 | 2010-09-30 | 31 |
| 49 | 2010-06-30 | 28 |
| 50 | 2010-03-31 | 21 |
| 52 | 2009-09-30 | 46 |
| 53 | 2009-06-30 | 27 |
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.
GameStop = yf.Ticker('GME')
Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.
gme_data = GameStop.history(period = 'max')
print(gme_data)
Open High Low Close Volume Dividends \
Date
2002-02-13 1.620129 1.693350 1.603296 1.691667 76216000 0.0
2002-02-14 1.712707 1.716074 1.670626 1.683250 11021600 0.0
2002-02-15 1.683250 1.687458 1.658001 1.674834 8389600 0.0
2002-02-19 1.666418 1.666418 1.578047 1.607504 7410400 0.0
2002-02-20 1.615921 1.662210 1.603296 1.662210 6892800 0.0
... ... ... ... ... ... ...
2024-01-22 14.500000 15.160000 14.300000 14.900000 3606500 0.0
2024-01-23 15.000000 15.020000 14.050000 14.180000 3495300 0.0
2024-01-24 14.280000 14.380000 13.820000 13.950000 2513800 0.0
2024-01-25 13.970000 14.540000 13.920000 14.520000 3631600 0.0
2024-01-26 14.440000 14.720000 14.420000 14.520100 1398538 0.0
Stock Splits
Date
2002-02-13 0.0
2002-02-14 0.0
2002-02-15 0.0
2002-02-19 0.0
2002-02-20 0.0
... ...
2024-01-22 0.0
2024-01-23 0.0
2024-01-24 0.0
2024-01-25 0.0
2024-01-26 0.0
[5526 rows x 7 columns]
Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.
gme_data.reset_index(inplace=True)
gme_data.head(5)
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 | 1.620129 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 | 1.712707 | 1.716074 | 1.670626 | 1.683250 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 | 1.683250 | 1.687458 | 1.658001 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 | 1.615921 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data = requests.get(url).text
Parse the html data using beautiful_soup.
soup = BeautifulSoup(html_data,"html.parser")
Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
gme_revenue = pd.DataFrame(columns=['Date','Revenue'])
for row in soup.find_all("tbody")[1].find_all('tr'):
col = row.find_all('td')
date = col[0].text
revenue = col[1].text
gme_revenue = gme_revenue.append({'Date':date,'Revenue':revenue},ignore_index = True)
gme_revenue
#Remove comma and dollar sign
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")
print(gme_revenue)
Date Revenue 0 2020-04-30 1021 1 2020-01-31 2194 2 2019-10-31 1439 3 2019-07-31 1286 4 2019-04-30 1548 .. ... ... 57 2006-01-31 1667 58 2005-10-31 534 59 2005-07-31 416 60 2005-04-30 475 61 2005-01-31 709 [62 rows x 2 columns]
Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.
gme_revenue.tail(5)
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.
make_graph(tesla_data, tesla_revenue,'Tesla')
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /tmp/ipykernel_2370/3781297139.py in <module> ----> 1 make_graph(tesla_data, tesla_revenue,'Tesla') /tmp/ipykernel_2370/2068038883.py in make_graph(stock_data, revenue_data, stock) 1 def make_graph(stock_data, revenue_data, stock): 2 fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3) ----> 3 stock_data_specific = stock_data[stock_data.Date <= '2021--06-14'] 4 revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30'] 5 fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1) ~/conda/envs/python/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name) 5485 ): 5486 return self[name] -> 5487 return object.__getattribute__(self, name) 5488 5489 def __setattr__(self, name: str, value) -> None: AttributeError: 'DataFrame' object has no attribute 'Date'
Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.
make_graph(gme_data, gme_revenue, 'GameStop')
Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Azim Hirjani
| Date (YYYY-MM-DD) | Version | Changed By | Change Description |
|---|---|---|---|
| 2022-02-28 | 1.2 | Lakshmi Holla | Changed the URL of GameStop |
| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |
| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |